# Chance-Constrained DC Optimal Power Flow Using Constraint-Informed Statistical Estimation

## TL;DR

This paper introduces a new method for chance-constrained DC optimal power flow that reduces complexity by focusing on aggregate and line errors, improving accuracy and efficiency over traditional high-dimensional distribution modeling.

## Contribution

It proposes a constraint-informed uncertainty estimation approach that simplifies the modeling process by targeting lower-dimensional error distributions, enhancing optimization performance.

## Key findings

- Significant reduction in modeling complexity.
- Improved statistical accuracy over existing methods.
- Enhanced optimization results on real-world data.

## Abstract

Chance-constrained optimization has emerged as a promising framework for managing uncertainties in power systems. This work advances its application to the DC Optimal Power Flow (DC-OPF) model, developing a novel approach to uncertainty modeling and estimation. Current methods typically tackle these problems by first modeling random nodal injections using high-dimensional statistical distributions that scale with the number of buses, followed by deriving deterministic reformulations of the probabilistic constraints. We propose an alternative methodology that exploits the constraint structure to inform the uncertainties to be estimated, enabling significant dimensionality reduction. Rather than learning joint distributions of net-load forecast errors across units, we instead directly model the one-dimensional aggregate system forecast error and two-dimensional line errors weighted by power transfer distribution factors. We evaluate our approach under both Gaussian and non-Gaussian distributions on synthetic and real-world datasets, demonstrating significant improvements in statistical accuracy and optimization performance compared to existing methods.